Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

Abstract

In this work quantitative structure-retention relationship models were developed to predict the solute retention factors in supercritical fluid chromatography (SFC) in various organic modifiers. Data set contains the retention data of 35 various organic compounds in 0, 2, 4 and 6% of methanol in mobile phase. The obtained 140 data points were divided into training, internal and external test sets which have 93, 23 and 24 retention data. The diversity validation test was performed on data the set to ensure that the structure of the training and/or test sets can represent those of the whole ones. Descriptors which were selected by stepwise multiple linear regression (MLR) methods are: the percent of organic modifier in mobile phase, salvation connectivity index chi-2, salvation connectivity index chi-5, H attached to heteroatom, the 2(nd) structural information content and polarity parameter. These descriptors were used as features in generation of linear and non-linear models using MLR and artificial neural network (ANN) methods, respectively. The root mean square error of MLR model are 0.116, 0.138 and 0.260 for training, internal and external test sets, respectively, while these values are 0.036, 0.097 and 0.244 for ANN model, respectively. Comparison between these values and other statistical parameters obtained from these two models reveals the credibility of ANN in prediction of solute retention factors in SFC.

Supercritical Fluid Chromatography

“…A quantitative structure retention relationship model to predict SFC retention of some organic compounds in various percents of organic modifiers in the MP using linear and nonlinear feature mapping techniques was published (60). The data set contained retention information for 35 various organic compounds in a MP which contained 0, 2, 4, and 6% methanol.…”

Development of QSAR for Antimicrobial Activity of Substituted Benzimidazoles

“…Molecular descriptors like WAP [11], 1xsol [12], BAC [13] were calculated for each compound of the dataset (1-45) using DRAGON 6.0 software [8]. In order to develop quantitative model for the prediction of antimicrobial activity of 45 benzimidazole derivatives against Escherichia coli (E. coli), Staphylococcus aureus (S. aureus) and Candida albicans (C. albicans) MLR analysis was used in order to get best QSAR model.…”

Quantitative structure-retention relationship prediction of Kováts retention index of some organic acids

“…In this study, diversity analysis was performed on the data set to make sure that the structures of the training and/or test sets can represent those of the whole ones. The theory of the diversity analysis was explained in reference [32]. For the whole of data set, the mean distances of samples in GA selected descriptor space were calculated and plotted versus the values I obs in Fig.…”